https://atlan.com

{
  "url": "https://atlan.com",
  "timestamp": 1788857874586,
  "fetch": {
    "mode": "simple",
    "timeMs": 54,
    "htmlSizeBytes": 343525,
    "supportsMarkdown": true,
    "markdownAgents": {
      "contentNegotiation": true,
      "mdUrl": {
        "found": false,
        "url": null
      },
      "linkTag": {
        "found": false,
        "url": null
      },
      "linkHeader": {
        "found": false,
        "url": null
      },
      "responseHeaders": {
        "contentSignal": "ai-train=yes, search=yes, ai-input=yes",
        "xMarkdownTokens": "8592",
        "vary": "Accept-Encoding, accept"
      },
      "frontmatter": {
        "present": true,
        "fields": [
          "title",
          "description",
          "image"
        ],
        "level": "enriched"
      },
      "level": "application",
      "contentNegotiationMediaType": "text/markdown",
      "properMediaType": true
    },
    "statusCode": 200
  },
  "extraction": {
    "title": "Atlan - The Context Layer for AI",
    "excerpt": "The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance to act on trusted data.",
    "byline": null,
    "siteName": "Atlan",
    "lang": "en",
    "contentLength": 2632,
    "metadata": {
      "description": "The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance to act on trusted data.",
      "ogTitle": "Atlan - The Context Layer for AI",
      "ogDescription": "The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance to act on trusted data.",
      "ogImage": "https://website-assets.atlan.com/img/home-2026/hp-2026-og.png",
      "ogType": "Website",
      "canonical": "https://atlan.com/",
      "lang": "en",
      "schemas": [
        {
          "@type": "Organization",
          "@id": "https://atlan.com/#organization",
          "name": "Atlan",
          "legalName": "Atlan Pte. Ltd.",
          "url": "https://atlan.com/",
          "logo": {
            "@type": "ImageObject",
            "@id": "https://atlan.com/#logo",
            "url": "https://website-assets.atlan.com/img/atlan-blue.svg",
            "width": 180,
            "height": 60,
            "caption": "Atlan Logo"
          },
          "slogan": "The missing context layer for enterprise AI.",
          "description": "Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.",
          "foundingDate": "2019",
          "foundingLocation": {
            "@type": "Place",
            "name": "Singapore"
          },
          "founders": [
            {
              "@type": "Person",
              "@id": "https://atlan.com/authors/prukalpa-sankar/#person",
              "name": "Prukalpa Sankar",
              "jobTitle": "Co-founder & Co-CEO",
              "sameAs": [
                "https://www.linkedin.com/in/prukalpa/",
                "https://www.wikidata.org/wiki/Q140315534"
              ]
            },
            {
              "@type": "Person",
              "@id": "https://atlan.com/#varun-banka",
              "name": "Varun Banka",
              "jobTitle": "Co-founder & Co-CEO",
              "sameAs": [
                "https://www.linkedin.com/in/varunbanka/",
                "https://www.wikidata.org/wiki/Q140315581"
              ]
            }
          ],
          "sameAs": [
            "https://www.wikidata.org/wiki/Q137792087",
            "https://www.linkedin.com/company/atlan-hq/",
            "https://twitter.com/AtlanHQ",
            "https://www.facebook.com/atlanhq/",
            "https://www.youtube.com/@atlanHQ",
            "https://www.crunchbase.com/organization/atlan-239d",
            "https://www.gartner.com/reviews/vendor/atlan",
            "https://www.g2.com/products/atlan/reviews"
          ],
          "contactPoint": {
            "@type": "ContactPoint",
            "contactType": "sales",
            "url": "https://atlan.com/forms/talk-to-sales-contact/"
          },
          "knowsAbout": [
            "Enterprise data governance",
            "AI context layer",
            "Metadata management",
            "Data lineage",
            "Context engineering",
            "Data catalog",
            "AI agents for enterprise data",
            "Enterprise data graph",
            "Data observability",
            "Model Context Protocol (MCP)"
          ]
        },
        {
          "@type": "WebSite",
          "@id": "https://atlan.com/#website",
          "name": "Atlan",
          "url": "https://atlan.com/",
          "description": "The missing context layer for enterprise AI.",
          "publisher": {
            "@id": "https://atlan.com/#organization"
          },
          "inLanguage": "en-US"
        },
        {
          "@type": "WebPage",
          "@id": "https://atlan.com/#webpage",
          "name": "Atlan - The Context Layer for AI",
          "description": "The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance to act on trusted data.",
          "url": "https://atlan.com/",
          "inLanguage": "en-US",
          "dateModified": "2026-04-09",
          "isAccessibleForFree": true,
          "primaryImageOfPage": {
            "@type": "ImageObject",
            "url": "https://website-assets.atlan.com/img/home-2026/hp-2026-og.png",
            "urlWidth": 1200,
            "urlHeight": 630,
            "width": 1200,
            "height": 630
          },
          "mainEntity": {
            "@id": "https://atlan.com/#organization"
          },
          "isPartOf": {
            "@id": "https://atlan.com/#website"
          },
          "publisher": {
            "@id": "https://atlan.com/#organization"
          }
        },
        {
          "@type": "FAQPage",
          "@id": "https://atlan.com/#faqpage",
          "mainEntity": [
            {
              "@type": "Question",
              "name": "What is Atlan?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan is the context layer for enterprise AI. It sits between your business systems and your AI agents, connecting lineage from data pipelines, business definitions from BI tools and SQL logic, knowledge from SOPs, quality scores, and access policies into a unified context store. Every agent and analyst queries that context store directly — no manual context-building per use case. Gartner named Atlan a Leader in the 2025 Metadata Management and 2026 Data and Analytics Governance Magic Quadrants. Forrester did the same in its 2024 Enterprise Data Catalogs and 2025 Data Governance Solutions Waves. The only platform recognized across all four."
              }
            },
            {
              "@type": "Question",
              "name": "What does Atlan do for enterprise AI?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan gives every AI agent the enterprise context it needs: the business definitions behind column names, the lineage behind every output, and the access policies behind every query. Without this, agents hallucinate, misclassify sensitive records, or return answers compliance teams reject. Every AI output is traceable — every answer points back to the data, the definition, and who certified it."
              }
            },
            {
              "@type": "Question",
              "name": "What is an enterprise context layer?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "An enterprise context layer sits between your business systems and your AI stack. It unifies context from across the business — lineage, semantic definitions, SOPs, access controls, usage patterns — into a single graph that agents and analysts query in real time. Without one, every new agent deployment starts with months of manual context-building. With one, every new agent inherits the organization's full institutional memory on day one."
              }
            },
            {
              "@type": "Question",
              "name": "How does the context pipeline work?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Four stages: unify, enrich, certify, activate. Atlan unifies metadata from native connectors — data warehouses, BI tools, pipeline orchestrators like dbt and Airflow. Context Agents auto-generate descriptions, metrics, and business ontology across the full data graph. Human experts review and certify — human-on-the-loop, not out of the loop. Certified context activates to every agent and tool via MCP, SQL, and open APIs. Evals and traces feed back in with each cycle, so context quality compounds over time."
              }
            },
            {
              "@type": "Question",
              "name": "How does Atlan work with AI agents?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "AI agents get enterprise context through Atlan's MCP server, SQL interface, and open APIs. A query returns the data graph, business definitions, lineage, and access policies for that specific task. Context repos version and package this knowledge, so every new agent starts with the organization's full institutional memory instead of a blank slate. No context hardcoded per use case. No starting over."
              }
            },
            {
              "@type": "Question",
              "name": "Which enterprise systems does Atlan connect to?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan connects natively to 80+ enterprise systems: Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI, and Postgres, among others. Once connected, lineage, query history, BI semantics, tags, and quality signals flow in automatically through scheduled and event-based workflows — no manual mapping required. Atlan also layers on top of existing catalogs like Microsoft Purview and Snowflake Horizon, pulling their metadata into a unified context layer."
              }
            },
            {
              "@type": "Question",
              "name": "Who uses Atlan?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan is deployed at enterprises including General Motors, Workday, Nasdaq, Mastercard, and Virgin Media O2. AI leaders use it to give agents governed access to enterprise context. Data engineers automate lineage and discovery. Governance teams enforce policies at the asset level. AI platform teams build and deploy agents faster because business logic is already in the context layer — not scattered across prompt files and wikis."
              }
            },
            {
              "@type": "Question",
              "name": "What analyst recognition has Atlan received?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan is the only platform named a Leader in all four major analyst evaluations for metadata and data governance: Gartner's 2025 Metadata Management Magic Quadrant, Gartner's 2026 Data and Analytics Governance Magic Quadrant, Forrester's 2024 Enterprise Data Catalogs Wave, and Forrester's 2025 Data Governance Solutions Wave. No other platform has been recognized across all four."
              }
            },
            {
              "@type": "Question",
              "name": "How does Atlan work alongside my existing data tools?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Atlan layers on top of your existing data stack. Many enterprises run Atlan alongside Microsoft Purview or Snowflake Horizon or Databricks Unity Catalog — pulling metadata from all into a unified context layer rather than rebuilding from scratch. Built on open APIs and Iceberg-native formats, context stored in Atlan stays portable: it is not locked to any vendor's proprietary schema. Switch AI frameworks, add new systems, or consolidate tools — the context layer moves with you."
              }
            },
            {
              "@type": "Question",
              "name": "How does Atlan approach context engineering?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Context engineering is the practice of selecting, structuring, and delivering the specific knowledge an AI agent needs at each step of a task. Most teams do this manually for each agent — months of work, duplicated across every use case. Atlan automates it: context from 80+ systems is unified, Context Agents auto-generate descriptions, metrics, and ontology across the full data graph, human experts certify, and certified context activates via MCP, SQL, and APIs. In April 2026, Context Agents generated 690K+ descriptions across 50+ enterprise customers — 87% rated on par or better than human writing. Every eval and trace feeds back in. Context quality compounds with each cycle."
              }
            },
            {
              "@type": "Question",
              "name": "How do teams get started with Atlan?",
              "acceptedAnswer": {
                "@type": "Answer",
                "text": "Start with a Context Workshop: Atlan's team maps your data and AI architecture, designs a context layer for a priority use case, and sets a measurable baseline. From there, a four-week Context Sprint delivers a working agent and accuracy results you can compare directly against your current approach. Most teams see the first value in weeks."
              }
            }
          ]
        }
      ],
      "robotsMeta": "index, follow",
      "author": null,
      "generator": null,
      "markdownAlternateHref": null
    }
  },
  "markdown": "## Your AI doesn't know your\nbusiness.\nLet’s fix that.\n\nBuild a shared understanding of your data, your business logic, and your institutional knowledge, and make it available to every AI tool you run.\n\n## Trusted by\nAI-forward enterprises\n\n![Mastercard](https://atlan.com/images/events-speakers/mastercard-h-logo.webp)\n\n![Hubspot](https://website-assets.atlan.com/img/home-2026/hp-2026-hubspot-logo-color.svg)\n\n![Zoom](https://website-assets.atlan.com/img/home-2026/hp-2026-zoom-logo-color.svg)\n\n![Dropbox](https://website-assets.atlan.com/img/home-2026/hp-2026-dropbox-logo-color.svg)\n\n![Autodesk](https://website-assets.atlan.com/img/home-2026/hp-2026-autodesk-logo-color.svg)\n\n![Nasdaq](https://website-assets.atlan.com/img/home-2026/hp-2026-nasdaq-logo-color.svg)\n\n![Fox](https://website-assets.atlan.com/img/home-2026/hp-2026-fox-logo-color.svg)\n\n![Marriott](https://website-assets.atlan.com/img/home-2026/hp-2026-marriott-2-logo-color.svg)\n\n![GitLab](https://website-assets.atlan.com/img/home-2026/hp-2026-gitlab-logo-color.svg)\n\n![Virgin Media O2](https://website-assets.atlan.com/img/home-2026/hp-2026-vmo2-logo-color.svg)\n\n![Unilever](https://website-assets.atlan.com/img/home-2026/hp-2026-unilever-logo-color.svg)\n\n![Workday](https://website-assets.atlan.com/img/home-2026/hp-2026-workday-logo-color.svg)\n\n![Elastic](https://website-assets.atlan.com/img/home-2026/hp-2026-elastic-logo-color.svg)\n\n![NHS](https://website-assets.atlan.com/img/home-2026/hp-2026-nhs-logo-color.svg)\n\n![Affirm](https://website-assets.atlan.com/img/home-2026/hp-2026-affirm-logo-color.svg)\n\n![General Motors](https://website-assets.atlan.com/img/home-2026/hp-2026-gm-logo-color.svg)\n\n![Easyjet](https://website-assets.atlan.com/img/home-2026/hp-2026-easyjet-logo-color.svg)\n\n![Medtronic](https://website-assets.atlan.com/img/home-2026/hp-2026-medtronic-logo-color.svg)\n\n![New York Life](https://website-assets.atlan.com/img/home-2026/hp-2026-new-york-life-logo-color.svg)\n\n![Grainger](https://website-assets.atlan.com/img/home-2026/hp-2026-grainger-logo-color.svg)\n\nThe Observation\n\n## Enterprise AI fails not because of the model, but because of missing [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_missing_context \"Context/26, our community conference\")\n\nWe've spent years studying how enterprises deploy AI agents. The pattern is consistent: teams build impressive prototypes, but hit a wall when moving to production.\n\nThe wall isn't the models. It’s that no agent can reason effectively about a business it doesn't understand — what your data means, how your teams work, how your company defines \"revenue\" compared to the rest of the world.\n\nKey Insight\n\nWhen every organization has access to the same intelligence, **context becomes the differentiator**. The enterprise that best articulates its own knowledge — its data, its processes, its meaning — will build AI that's most useful to its people.\n\nWhy Customers Love Atlan\n\n## The only proven way to create [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_create_context \"Context/26, our community conference\")\n\n1.  Watch Video\n\n    ![Sridher Arumugham](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-sridher.webp)![Sridher Arumugham](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-sridher-arumugham.webp)\n\n2.  Watch Video\n\n    ![Kiran Panja](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-kiran.webp)![Kiran Panja](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-kiran.webp)\n\n3.  Watch Video\n\n    ![Andrew Reiskind](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-andrew.webp)![Andrew Reiskind](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-andrew.webp)\n\n4.  Watch Video\n\n    ![Mauro Flores](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-mauro.webp)![Mauro Flores](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-mauro.webp)\n\nItem 1 of 4\n\n![Company logo](https://website-assets.atlan.com/img/regovern-2025/digikey-logo.svg)\n\n![Company logo](https://website-assets.atlan.com/img/regovern-2025/cme-logo.svg)\n\n![Company logo](https://website-assets.atlan.com/img/home-2026/hp-2026-mastercard-logo-color.svg)\n\n![Company logo](https://website-assets.atlan.com/img/home-2026/hp-2026-vmo2-logo-color.svg)\n\nIndustry Recognition\n\n## A leader across every [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_leader_category \"Context/26, our community conference\") category\n\n“The Metadata Lakehouse forms the core foundation, built on an open and highly performant architecture. It is designed to be Iceberg-native and includes a knowledge graph for business domains, vector storage, and analytics, which is purpose-built for AI.”\n\n“Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. They take a partnership and co-innovation based approach, which is reflected in their App Framework as a marketplace for context.”\n\n[![Forrester Wave Leader 2024](https://website-assets.atlan.com/img/forrester-wave-badge.webp)](https://atlan.com/forrester-wave-2024/)\n\n[![Forrester Wave Leader 2025](https://website-assets.atlan.com/img/forrester-wave-2025/fw-2025-wave-leader-badge_1_optimized.webp)](https://atlan.com/forrester-wave/)\n\n[![Forrester Wave Customer Favorite 2025](https://website-assets.atlan.com/img/forrester-wave-2025/fw-2025-customer-fav-badge_1_optimized.webp)](https://atlan.com/forrester-wave/)\n\n### A Leader and a Customer Favourite in the Forrester Wave™\n\nData & Analytics Governance Solutions and Enterprise Data Catalogs\n\nWhat We Believe\n\n## [Context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_worthy_moments \"Context/26, our community conference\") will make AI worthy of\nhumanity’s most important moments\n\nWe hold strong convictions about how the the context layer should be built.\nThese shape every decision we make.\n\n![Context is a Team Sport](https://website-assets.atlan.com/img/navbar-icons/prod-nav-personalize-blue.svg)\n\n### Context is a Team Sport\n\nYour frontline teams — not just engineers — should be able to read, question, and improve the context that shapes how AI behaves. The best context comes from people working together.\n\n![AI-Native, Built for Change](https://website-assets.atlan.com/img/navbar-icons/prod-nav-ai-gov-blue.svg)\n\n### AI-Native, Built for Change\n\nYour context layer should outlive any single technology cycle. Today it powers MCP and A2A. Tomorrow, whatever protocol comes next — no migrations, no rebuilds.\n\n![Open & Portable](https://website-assets.atlan.com/img/navbar-icons/prod-nav-connector-icon.svg)\n\n### Open & Portable\n\nYour context should move freely across agents, models, and clouds. You should never be locked into a single vendor's representation of your own knowledge.\n\nFAQ\n\n## Frequently asked questions\n\n## Bridge the [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_bridge_gap \"Context/26, our community conference\") gap.\nShip AI that works.\n",
  "fullPageMarkdown": "Atlan - The Context Layer for AI\n\n\\# ·x - # x#= ++x % #x - =+·# + - +%@ - % ~~++ x# ~- %· %+ xxx · ·# %# ~ - # ~ %-@ -x ·+~ +#-- - = -++ =· = --x ·= % @ ~%\n\nOct 28\n\nContext Conference: Join leaders from **Google**, **AstraZeneca**, and **Verizon** who are defining the context layer.\n\n[Register](https://atlan.com/context-conference/?utm_source=hello-bar&utm_medium=website&utm_campaign=context-conference)\n\n[![Atlan Logo](https://website-assets.atlan.com/img/atlan-blue.svg)](https://atlan.com/)\n\n[Talk to Us](https://atlan.com/forms/talk-to-sales-contact/) [The rise of the agentic data catalog ](https://atlan.com/context-in-practice-can-ai-document-your-data/?utm_source=navbar&utm_medium=website&utm_campaign=context-in-practice)\n\nThe [Context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=hero_pill \"Context/26, our community conference\") Layer for AI\n\n# Your AI doesn't know your\nbusiness.\nLet’s fix that.\n\nBuild a shared understanding of your data, your business logic, and your institutional knowledge, and make it available to every AI tool you run.\n\n[Talk to Us](https://atlan.com/forms/talk-to-sales-contact/)\n\nSee How it Works\n\nInterfaces &\nAgents\n\nOpen &\nPortable\nContext\n\nBusiness\nSystems\n\nCustom Agents\n\n![Salesforce Agentforce](https://atlan.com/img/context-studio/salesforce-cloud.svg)\n\n![Snowflake](https://atlan.com/img/context-studio/Snowflake.svg)\n\n![AI assistant](https://atlan.com/img/context-studio/sparkle.svg)\n\nVertical Agents\n\n![Decagon](https://atlan.com/img/context-studio/decagon.svg)\n\n![Sierra](https://atlan.com/img/context-studio/sierra.svg)\n\n![Writer](https://atlan.com/img/context-studio/writer.svg)\n\nGeneral Purpose Agents\n\n![Anthropic Claude](https://atlan.com/img/context-studio/claude.svg)\n\n![OpenAI](https://atlan.com/img/context-studio/openai.svg)\n\nTools\n\n![Slack](https://atlan.com/img/context-studio/Slack.svg)\n\n![Microsoft Teams](https://atlan.com/img/context-studio/msteams.svg)\n\n![Jira](https://atlan.com/img/context-studio/jira.svg)\n\nContext Repo\n\nContext Repo\n\nContext Repo\n\nEnterprise Context Layer\n\nAI-Ready Data & Knowledge Graph\n\nThe integrated, trusted, and AI-ready representation of an enterprise's data and knowledge assets.\n\n[Connectors](https://atlan.com/connectors/)[Data Lineage](https://atlan.com/data-lineage/)\n\nSemantics & Ontology\n\nThe map of the business: its entities, metrics, and relationships.\n\n[Context Agents](https://atlan.com/context-agents/)\n\nAgent Skills\n\nReusable, versioned, testable units of procedural knowledge.\n\n[Context Engineering Studio](https://atlan.com/context-engineering-studio/)\n\nSystems of Record\n\n![Salesforce](https://atlan.com/img/context-studio/salesforce-cloud.svg)\n\n![SAP](https://atlan.com/img/context-studio/sap.svg)\n\n![HubSpot](https://atlan.com/img/context-studio/hubspot.svg)\n\nSystems of Semantics\n\n![Looker](https://atlan.com/img/context-studio/Looker.svg)\n\n![Microsoft Power BI](https://atlan.com/img/context-studio/powerbi.svg)\n\n![Tableau](https://atlan.com/img/context-studio/Tableau.svg)\n\nSystems of Data\n\n![Snowflake](https://atlan.com/img/context-studio/Snowflake.svg)\n\n![Databricks](https://atlan.com/img/context-studio/Databricks.svg)\n\n![Google BigQuery](https://atlan.com/img/context-studio/bigquery-hex.svg)\n\n![Google Cloud](https://atlan.com/img/context-studio/google.svg)\n\nSystems of Knowledge\n\n![Confluence](https://atlan.com/img/context-studio/Confluence.svg)\n\n![Microsoft SharePoint](https://atlan.com/img/context-studio/sharepoint.svg)\n\n![Google Drive](https://atlan.com/img/context-studio/googledrive.svg)\n\n## Trusted by\nAI-forward enterprises\n\n![Mastercard](https://atlan.com/images/events-speakers/mastercard-h-logo.webp)\n\nSpotlight\n\nUPCOMING • 10 SEPT · 11 AM ET### Traditional data cataloging is dead.\n\nJoin us live for practitioner takes on making data AI-ready, and how an agentic data catalog generated 2M+ descriptions and saved 210,000+ hours across 200+ organizations.\n\n![spinner](https://website-assets.atlan.com/img/spinner.svg)\n\n[View session details](https://atlan.com/context-in-practice-can-ai-document-your-data/?utm_source=homepage-logo-grid&utm_medium=website&utm_campaign=context-in-practice)\n\n![Hubspot](https://website-assets.atlan.com/img/home-2026/hp-2026-hubspot-logo-color.svg)\n\nStory\n\n![Zoom](https://website-assets.atlan.com/img/home-2026/hp-2026-zoom-logo-color.svg)\n\n![Dropbox](https://website-assets.atlan.com/img/home-2026/hp-2026-dropbox-logo-color.svg)\n\n![Autodesk](https://website-assets.atlan.com/img/home-2026/hp-2026-autodesk-logo-color.svg)\n\n![Nasdaq](https://website-assets.atlan.com/img/home-2026/hp-2026-nasdaq-logo-color.svg)\n\n![Fox](https://website-assets.atlan.com/img/home-2026/hp-2026-fox-logo-color.svg)\n\nStory\n\n![Marriott](https://website-assets.atlan.com/img/home-2026/hp-2026-marriott-2-logo-color.svg)\n\n![GitLab](https://website-assets.atlan.com/img/home-2026/hp-2026-gitlab-logo-color.svg)\n\n![Virgin Media O2](https://website-assets.atlan.com/img/home-2026/hp-2026-vmo2-logo-color.svg)\n\nStory\n\n![Unilever](https://website-assets.atlan.com/img/home-2026/hp-2026-unilever-logo-color.svg)\n\n![Workday](https://website-assets.atlan.com/img/home-2026/hp-2026-workday-logo-color.svg)\n\nStory\n\n![Elastic](https://website-assets.atlan.com/img/home-2026/hp-2026-elastic-logo-color.svg)\n\nStory\n\n![NHS](https://website-assets.atlan.com/img/home-2026/hp-2026-nhs-logo-color.svg)\n\n![Affirm](https://website-assets.atlan.com/img/home-2026/hp-2026-affirm-logo-color.svg)\n\n![General Motors](https://website-assets.atlan.com/img/home-2026/hp-2026-gm-logo-color.svg)\n\nStory\n\n![Easyjet](https://website-assets.atlan.com/img/home-2026/hp-2026-easyjet-logo-color.svg)\n\n![Medtronic](https://website-assets.atlan.com/img/home-2026/hp-2026-medtronic-logo-color.svg)\n\n![New York Life](https://website-assets.atlan.com/img/home-2026/hp-2026-new-york-life-logo-color.svg)\n\n![Grainger](https://website-assets.atlan.com/img/home-2026/hp-2026-grainger-logo-color.svg)\n\n[See All Customer Stories](https://atlan.com/customers/)\n\nThe Observation## Enterprise AI fails not because of the model, but because of missing [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_missing_context \"Context/26, our community conference\")\n\nWe've spent years studying how enterprises deploy AI agents. The pattern is consistent: teams build impressive prototypes, but hit a wall when moving to production.\n\nThe wall isn't the models. It’s that no agent can reason effectively about a business it doesn't understand — what your data means, how your teams work, how your company defines \"revenue\" compared to the rest of the world.\n\nKey Insight\n\nWhen every organization has access to the same intelligence, **context becomes the differentiator**. The enterprise that best articulates its own knowledge — its data, its processes, its meaning — will build AI that's most useful to its people.\n\n“We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. We had no way to interpret human language against the structure of the data.”\n\nJoe DosSantos\n\nVP, Enterprise Data & Analytics\n\n![Company logo](https://website-assets.atlan.com/img/home-2026/hp-2026-workday-logo-white.svg)\n\nWatch Video\n\n![Speaker](https://atlan.com/img/home-2026/hp-2026-observation-joe-dossantos-md.webp)\n\nThe AI Context Gap\n\n## One question for AI.\nThree kinds of [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_three_kinds \"Context/26, our community conference\").\n\nThrough our work with enterprises, we've found that even a simple agent task requires three kinds of context — knowledge, expertise, and norms — working together.\n\nTalk-to-Data AgentOperational Agent\n\nWhy is drive-through time up this week?Why is drive-through time up this week?\n\nQuestion It Raises\n\nContext Type\n\nAnswer It Needs\n\nKnowledge\n\nWhat does \"drive-through time\" mean?\n\n**avg\\_dt\\_secs**, not the POS number Finance uses\n\nExpertise\n\nHow do you investigate this?\n\nCheck **seasonality** and nearby launches before naming a cause\n\nNorms\n\nWho's asking, and what can they see?\n\nStore manager sees **their store only**; VP Ops sees the full chain\n\nWhy Customers Love Atlan## The only proven way to create [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_create_context \"Context/26, our community conference\")\n\n1.  Watch Video\n\n    ![Sridher Arumugham](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-sridher.webp)![Sridher Arumugham](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-sridher-arumugham.webp)\n\n2.  Watch Video\n\n    ![Kiran Panja](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-kiran.webp)![Kiran Panja](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-kiran.webp)\n\n3.  Watch Video\n\n    ![Andrew Reiskind](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-andrew.webp)![Andrew Reiskind](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-andrew.webp)\n\n4.  Watch Video\n\n    ![Mauro Flores](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-mobile-mauro.webp)![Mauro Flores](https://website-assets.atlan.com/img/home-2026/hp-2026-testimonial-desktop-mauro.webp)\n\nItem 1 of 4\n\n![Company logo](https://website-assets.atlan.com/img/regovern-2025/digikey-logo.svg)\n\n![Company logo](https://website-assets.atlan.com/img/regovern-2025/cme-logo.svg)\n\n![Company logo](https://website-assets.atlan.com/img/home-2026/hp-2026-mastercard-logo-color.svg)\n\n![Company logo](https://website-assets.atlan.com/img/home-2026/hp-2026-vmo2-logo-color.svg)\n\nThe Context Pipeline\n\n## [Context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_not_a_prompt \"Context/26, our community conference\") doesn't come from a prompt. It comes from a pipeline.\n\nWhat if every agent knew what your best analyst knows? Your business systems, data estate, and people already hold the context you need. The context pipeline makes it usable.\n\n![Unify](https://website-assets.atlan.com/img/home-2026/hp-2026-unify-pink-icon.svg)\n\nUNIFY\n\n### Unify business systems in the Enterprise Data Graph\n\n80+ connectors pull context across your entire data estate — warehouse SQL, BI definitions, and business applications — into one living graph. That graph is what everything else in the pipeline builds on.\n\nCatalogGovernanceLineageQualityGlossary\n\n“Within the first year after that we cataloged over 18 million assets, defined more than 1300 glossary terms. Atlan had lineage across our on-prem Oracle databases, BigQuery, and Looker..”\n\nKiran Panja\n\nManaging Director, Cloud & Data Engineering\n\n![CME Group](https://website-assets.atlan.com/img/regovern-2025/cme-logo.svg)\n\n![Bootstrap](https://website-assets.atlan.com/img/home-2026/hp-2026-bootstrap-pink-icon.svg)\n\nBOOTSTRAP\n\n### Let AI bootstrap your context layer\n\nAtlan’s AI agents read the Enterprise Data Graph — your SQL query history, BI semantics, and pipeline code — and generate asset descriptions, link business terms, and surface your top business questions. The first 80% of your context layer is ready before a human reviews a single line.\n\nDescription GeneratorTerm LinkageMetrics GeneratorSemantic ViewsOntology Generator\n\n“We’re scaling context development as much as possible, and where can we leverage Atlan AI to build the most robust definitions across our data estate.”\n\nTakashi Ueki\n\nHead of Enterprise Data & Analytics\n\n![Elastic](https://website-assets.atlan.com/img/home-2026/hp-2026-elastic-logo-color.svg)\n\n![Collaborate](https://website-assets.atlan.com/img/home-2026/hp-2026-collaborate-pink-icon.svg)\n\nCOLLABORATE\n\n### Humans resolve, annotate, and certify before context ships\n\nThe AI draft is a starting point, not the final word. Your domain experts resolve conflicts between sources, annotate edge cases, and certify what’s production-ready. What ships is what your team trusts.\n\nConflict ResolutionAnnotationLabellingCertificationFeedback Loops\n\n“Atlan gives us a UI that our community can use to edit, update and manage classifications as well as other metadata enrichments into a verified state.”\n\nSherri Adame\n\nEnterprise Data Governance Leader\n\n![General Motors](https://website-assets.atlan.com/img/home-2026/hp-2026-gm-logo-color.svg)\n\n![Activate](https://website-assets.atlan.com/img/home-2026/hp-2026-activate-pink-icon.svg)\n\nACTIVATE\n\n### Certified context flows to every AI agent across your stack\n\nProduction-ready context serves every downstream tool through SQL, APIs, and the Atlan MCP server. Evals, traces, and memory feed back into the pipeline and context gets sharper with every interaction.\n\nMCP ServerSQLAPIsSDKEvals & Traces\n\n“All of the work that we did to get to a shared language amongst people at Workday can be leveraged by AI via Atlan’s MCP server.”\n\nJoe DosSantos\n\nVP, Enterprise Data & Analytics\n\n![Workday](https://website-assets.atlan.com/img/home-2026/hp-2026-workday-logo-color.svg)\n\n![Previous](https://website-assets.atlan.com/img/arrow-right-blue.svg)![Next](https://website-assets.atlan.com/img/arrow-right-blue.svg)\n\nIndustry Recognition## A leader across every [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_leader_category \"Context/26, our community conference\") category\n\n![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-grid-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-momentum-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-grid-leader-enterprise-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-easiest-to-use-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-best-relationship-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-europe-regional-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-europe-high-performer-2025.svg)\n\n![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-grid-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-momentum-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-grid-leader-enterprise-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-easiest-to-use-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-best-relationship-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-europe-regional-leader-2025.svg)![G2 Badge](https://website-assets.atlan.com/img/g2/g2-summer-europe-high-performer-2025.svg)\n\n![G2 logo](https://website-assets.atlan.com/img/g2/g2-logo-transparent.svg)### 95% of G2 users see\nAtlan as a true partner[Read the G2 report](https://atlan.com/g2-leader/)\n\n![Analyst Report Graph](https://website-assets.atlan.com/img/home/homepage-wr-fw-2024-graph.webp)\n\n“The Metadata Lakehouse forms the core foundation, built on an open and highly performant architecture. It is designed to be Iceberg-native and includes a knowledge graph for business domains, vector storage, and analytics, which is purpose-built for AI.”\n\nLeader in the 2025 Gartner® Magic Quadrant™ for Metadata Management Solutions\n\n[Read the Gartner MQ report](https://atlan.com/gartner-magic-quadrant-metadata-management-solutions-2025/)\n\n![Analyst Report Graph](https://website-assets.atlan.com/img/home/homepage-wr-fw-2025-graph.webp)\n\n“Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. They take a partnership and co-innovation based approach, which is reflected in their App Framework as a marketplace for context.”\n\nLeader in the 2026 Gartner® Magic Quadrant™ for Data & Analytics Governance\n\n[Read the Gartner D&A report](https://atlan.com/gartner-magic-quadrant-data-governance-2026/)\n\n[![Forrester Wave Leader 2024](https://website-assets.atlan.com/img/forrester-wave-badge.webp)](https://atlan.com/forrester-wave-2024/)[![Forrester Wave Leader 2025](https://website-assets.atlan.com/img/forrester-wave-2025/fw-2025-wave-leader-badge_1_optimized.webp)](https://atlan.com/forrester-wave/)[![Forrester Wave Customer Favorite 2025](https://website-assets.atlan.com/img/forrester-wave-2025/fw-2025-customer-fav-badge_1_optimized.webp)](https://atlan.com/forrester-wave/)\n\n### A Leader and a Customer Favourite in the Forrester Wave™\n\nData & Analytics Governance Solutions and Enterprise Data Catalogs\n\nWhat We Believe## [Context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_worthy_moments \"Context/26, our community conference\") will make AI worthy of\nhumanity’s most important moments\n\nWe hold strong convictions about how the the context layer should be built.\nThese shape every decision we make.\n\n![Context is a Team Sport](https://website-assets.atlan.com/img/navbar-icons/prod-nav-personalize-blue.svg)\n\n### Context is a Team Sport\n\nYour frontline teams — not just engineers — should be able to read, question, and improve the context that shapes how AI behaves. The best context comes from people working together.\n\n![AI-Native, Built for Change](https://website-assets.atlan.com/img/navbar-icons/prod-nav-ai-gov-blue.svg)\n\n### AI-Native, Built for Change\n\nYour context layer should outlive any single technology cycle. Today it powers MCP and A2A. Tomorrow, whatever protocol comes next — no migrations, no rebuilds.\n\n![Open & Portable](https://website-assets.atlan.com/img/navbar-icons/prod-nav-connector-icon.svg)\n\n### Open & Portable\n\nYour context should move freely across agents, models, and clouds. You should never be locked into a single vendor's representation of your own knowledge.\n\nFAQ## Frequently asked questions\n\nWhat is Atlan?\n\nAtlan is the context layer for enterprise AI. It sits between your business systems and your AI agents, connecting lineage from data pipelines, business definitions from BI tools and SQL logic, knowledge from SOPs, quality scores, and access policies into a unified context store. Every agent and analyst queries that context store directly — no manual context-building per use case. Gartner named Atlan a Leader in the 2025 Metadata Management and 2026 Data and Analytics Governance Magic Quadrants. Forrester did the same in its 2024 Enterprise Data Catalogs and 2025 Data Governance Solutions Waves. The only platform recognized across all four.\n\nWhat does Atlan do for enterprise AI?\n\nAtlan gives every AI agent the enterprise context it needs: the business definitions behind column names, the lineage behind every output, and the access policies behind every query. Without this, agents hallucinate, misclassify sensitive records, or return answers compliance teams reject. Every AI output is traceable — every answer points back to the data, the definition, and who certified it.\n\nWhat is an enterprise context layer?\n\nAn enterprise context layer sits between your business systems and your AI stack. It unifies context from across the business — lineage, semantic definitions, SOPs, access controls, usage patterns — into a single graph that agents and analysts query in real time. Without one, every new agent deployment starts with months of manual context-building. With one, every new agent inherits the organization's full institutional memory on day one.\n\nHow does the context pipeline work?\n\nFour stages: unify, enrich, certify, activate. Atlan unifies metadata from native connectors — data warehouses, BI tools, pipeline orchestrators like dbt and Airflow. Context Agents auto-generate descriptions, metrics, and business ontology across the full data graph. Human experts review and certify — human-on-the-loop, not out of the loop. Certified context activates to every agent and tool via MCP, SQL, and open APIs. Evals and traces feed back in with each cycle, so context quality compounds over time.\n\nHow does Atlan work with AI agents?\n\nAI agents get enterprise context through Atlan's MCP server, SQL interface, and open APIs. A query returns the data graph, business definitions, lineage, and access policies for that specific task. Context repos version and package this knowledge, so every new agent starts with the organization's full institutional memory instead of a blank slate. No context hardcoded per use case. No starting over.\n\nWhich enterprise systems does Atlan connect to?\n\nAtlan connects natively to 80+ enterprise systems: Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI, and Postgres, among others. Once connected, lineage, query history, BI semantics, tags, and quality signals flow in automatically through scheduled and event-based workflows — no manual mapping required. Atlan also layers on top of existing catalogs like Microsoft Purview and Snowflake Horizon, pulling their metadata into a unified context layer.\n\nWho uses Atlan?\n\nAtlan is deployed at enterprises including General Motors, Workday, Nasdaq, Mastercard, and Virgin Media O2. AI leaders use it to give agents governed access to enterprise context. Data engineers automate lineage and discovery. Governance teams enforce policies at the asset level. AI platform teams build and deploy agents faster because business logic is already in the context layer — not scattered across prompt files and wikis.\n\nWhat analyst recognition has Atlan received?\n\nAtlan is the only platform named a Leader in all four major analyst evaluations for metadata and data governance: Gartner's 2025 Metadata Management Magic Quadrant, Gartner's 2026 Data and Analytics Governance Magic Quadrant, Forrester's 2024 Enterprise Data Catalogs Wave, and Forrester's 2025 Data Governance Solutions Wave. No other platform has been recognized across all four.\n\nHow does Atlan work alongside my existing data tools?\n\nAtlan layers on top of your existing data stack. Many enterprises run Atlan alongside Microsoft Purview or Snowflake Horizon or Databricks Unity Catalog — pulling metadata from all into a unified context layer rather than rebuilding from scratch. Built on open APIs and Iceberg-native formats, context stored in Atlan stays portable: it is not locked to any vendor's proprietary schema. Switch AI frameworks, add new systems, or consolidate tools — the context layer moves with you.\n\nHow does Atlan approach context engineering?\n\nContext engineering is the practice of selecting, structuring, and delivering the specific knowledge an AI agent needs at each step of a task. Most teams do this manually for each agent — months of work, duplicated across every use case. Atlan automates it: context from 80+ systems is unified, Context Agents auto-generate descriptions, metrics, and ontology across the full data graph, human experts certify, and certified context activates via MCP, SQL, and APIs. In April 2026, Context Agents generated 690K+ descriptions across 50+ enterprise customers — 87% rated on par or better than human writing. Every eval and trace feeds back in. Context quality compounds with each cycle.\n\nHow do teams get started with Atlan?\n\nStart with a Context Workshop: Atlan's team maps your data and AI architecture, designs a context layer for a priority use case, and sets a measurable baseline. From there, a four-week Context Sprint delivers a working agent and accuracy results you can compare directly against your current approach. Most teams see the first value in weeks.\n\n## Bridge the [context](https://atlan.com/context-26/?utm_source=atlan_website&utm_medium=easter_egg&utm_campaign=context26&utm_content=h2_bridge_gap \"Context/26, our community conference\") gap.\nShip AI that works.\n\n[Book a Demo](https://atlan.com/forms/talk-to-sales-contact/)\n\nSee How it Works\n\n![Trusted by leading enterprises](https://website-assets.atlan.com/img/home-2026/hp-2026-customer-logos-bottom-cta.svg)\n\n\\[Website env: production\\]\n\n{}\n",
  "markdownStats": {
    "images": 38,
    "links": 8,
    "tables": 0,
    "codeBlocks": 0,
    "headings": 12
  },
  "tokens": {
    "htmlTokens": 103856,
    "markdownTokens": 1918,
    "reduction": 101938,
    "reductionPercent": 98
  },
  "score": {
    "score": 85,
    "grade": "B",
    "rubricVersion": 3,
    "dimensions": {
      "accessibility": {
        "score": 90,
        "weight": 30,
        "grade": "A",
        "checks": {
          "content_without_js": {
            "score": 100,
            "weight": 55,
            "evidence": "proven",
            "details": "Content available without JavaScript"
          },
          "fast_content_position": {
            "score": 75,
            "weight": 25,
            "evidence": "plausible",
            "details": "Main content starts at 25% of HTML"
          },
          "reasonable_page_size": {
            "score": 80,
            "weight": 20,
            "evidence": "plausible",
            "details": "Page size: 335KB"
          }
        }
      },
      "aiDiscoverability": {
        "score": 83,
        "weight": 25,
        "grade": "B",
        "checks": {
          "robots_allows_ai_bots": {
            "score": 100,
            "weight": 35,
            "evidence": "proven",
            "details": "All major AI search bots allowed"
          },
          "supports_markdown_negotiation": {
            "score": 25,
            "weight": 20,
            "evidence": "plausible",
            "details": "Application level — Content negotiation",
            "mechanisms": {
              "negotiation": true,
              "mdUrl": false,
              "linkTag": false,
              "linkHeader": false
            }
          },
          "has_sitemap": {
            "score": 100,
            "weight": 15,
            "evidence": "plausible",
            "details": "Sitemap found"
          },
          "has_robots_txt": {
            "score": 100,
            "weight": 10,
            "evidence": "plausible",
            "details": "robots.txt exists"
          },
          "has_llms_txt": {
            "score": 100,
            "weight": 10,
            "evidence": "speculative",
            "details": "llms.txt exists and is valid"
          },
          "has_content_signals": {
            "score": 80,
            "weight": 10,
            "evidence": "speculative",
            "details": "robots.txt: search=yes, ai-input=yes, ai-train=yes | Policy included",
            "mechanisms": {
              "robotsTxt": true,
              "httpHeader": false,
              "policy": true
            }
          }
        }
      },
      "structuredData": {
        "score": 100,
        "weight": 20,
        "grade": "A",
        "checks": {
          "has_schema_org": {
            "score": 100,
            "weight": 35,
            "evidence": "proven",
            "details": "JSON-LD found: Organization, WebSite, WebPage, FAQPage"
          },
          "has_open_graph": {
            "score": 100,
            "weight": 20,
            "evidence": "plausible",
            "details": "All OG tags present"
          },
          "has_meta_description": {
            "score": 100,
            "weight": 20,
            "evidence": "plausible",
            "details": "Meta description: 142 chars"
          },
          "has_canonical_url": {
            "score": 100,
            "weight": 15,
            "evidence": "plausible",
            "details": "Canonical URL present"
          },
          "has_lang_attribute": {
            "score": 100,
            "weight": 10,
            "evidence": "plausible",
            "details": "lang=\"en\""
          }
        }
      },
      "semanticHtml": {
        "score": 80,
        "weight": 15,
        "grade": "B",
        "checks": {
          "proper_heading_hierarchy": {
            "score": 100,
            "weight": 30,
            "evidence": "plausible",
            "details": "Clean heading hierarchy"
          },
          "uses_article_or_main": {
            "score": 100,
            "weight": 25,
            "evidence": "plausible",
            "details": "Has <main>"
          },
          "semantic_elements": {
            "score": 10,
            "weight": 20,
            "evidence": "plausible",
            "details": "15 semantic elements, 482 divs (ratio: 3%)"
          },
          "meaningful_alt_texts": {
            "score": 92,
            "weight": 15,
            "evidence": "plausible",
            "details": "135/147 images with meaningful alt text (8 decorative, not counted)"
          },
          "low_div_nesting": {
            "score": 89,
            "weight": 10,
            "evidence": "speculative",
            "details": "Avg div depth: 6.1, max: 11"
          }
        }
      },
      "contentEfficiency": {
        "score": 50,
        "weight": 10,
        "grade": "D",
        "checks": {
          "token_reduction_ratio": {
            "score": 100,
            "weight": 40,
            "evidence": "speculative",
            "details": "98% token reduction (HTML→Markdown)"
          },
          "content_to_noise_ratio": {
            "score": 0,
            "weight": 30,
            "evidence": "speculative",
            "details": "Content ratio: 0.8% (2632 content chars / 343525 HTML bytes)"
          },
          "reasonable_page_weight": {
            "score": 50,
            "weight": 20,
            "evidence": "speculative",
            "details": "HTML size: 335KB"
          },
          "minimal_inline_styles": {
            "score": 0,
            "weight": 10,
            "evidence": "speculative",
            "details": "101/1417 elements with inline styles (7.1%)"
          }
        }
      }
    }
  },
  "recommendations": [
    {
      "id": "add_markdown_negotiation",
      "priority": "high",
      "category": "aiDiscoverability",
      "titleKey": "rec.add_markdown_negotiation.title",
      "descriptionKey": "rec.add_markdown_negotiation.description_partial",
      "howToKey": "rec.add_markdown_negotiation.howto",
      "howToStepKeys": [
        "rec.add_markdown_negotiation.step.md_url",
        "rec.add_markdown_negotiation.step.link_tag",
        "rec.add_markdown_negotiation.step.link_header"
      ],
      "effort": "significant",
      "estimatedImpact": 3.8,
      "maxImpact": 5,
      "evidence": "plausible",
      "checkScore": 25,
      "checkDetails": "Application level — Content negotiation"
    },
    {
      "id": "improve_content_ratio",
      "priority": "high",
      "category": "contentEfficiency",
      "titleKey": "rec.improve_content_ratio.title",
      "descriptionKey": "rec.improve_content_ratio.description",
      "howToKey": "rec.improve_content_ratio.howto",
      "howToStepKeys": null,
      "effort": "moderate",
      "estimatedImpact": 3,
      "maxImpact": 3,
      "evidence": "speculative",
      "checkScore": 0,
      "checkDetails": "Content ratio: 0.8% (2632 content chars / 343525 HTML bytes)"
    },
    {
      "id": "add_semantic_elements",
      "priority": "high",
      "category": "semanticHtml",
      "titleKey": "rec.add_semantic_elements.title",
      "descriptionKey": "rec.add_semantic_elements.description",
      "howToKey": "rec.add_semantic_elements.howto",
      "howToStepKeys": null,
      "effort": "moderate",
      "estimatedImpact": 2.7,
      "maxImpact": 3,
      "evidence": "plausible",
      "checkScore": 10,
      "checkDetails": "15 semantic elements, 482 divs (ratio: 3%)"
    },
    {
      "id": "remove_inline_styles",
      "priority": "medium",
      "category": "contentEfficiency",
      "titleKey": "rec.remove_inline_styles.title",
      "descriptionKey": "rec.remove_inline_styles.description",
      "howToKey": "rec.remove_inline_styles.howto",
      "howToStepKeys": null,
      "effort": "moderate",
      "estimatedImpact": 1,
      "maxImpact": 1,
      "evidence": "speculative",
      "checkScore": 0,
      "checkDetails": "101/1417 elements with inline styles (7.1%)"
    }
  ],
  "llmsTxtPreview": "# Atlan\n\n> Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches an…\n\n## Main\n- [Atlan - The Context Layer for AI](https://atlan.com): The missing context layer for enterprise AI. Atlan gives every AI agent the data graph, business logic, and governance…\n- [About us](https://atlan.com/about/)\n- [Connectors & Apps](https://atlan.com/connectors/)\n- [Data Lineage](https://atlan.com/data-lineage/)\n- [See all customer stories →](https://atlan.com/customers/)\n- [Resources](https://atlan.com/know/enterprise-context-layer/)\n- [Demo Videos](https://atlan.com/demos/)\n- [Newsroom](https://atlan.com/newsroom/)\n- [Careers](https://atlan.com/careers/)\n- [Events](https://atlan.com/events/)\n\n## Legal\n- [Privacy Notice](https://atlan.com/privacy-policy/)\n- [Cookie Policy](https://atlan.com/cookie-policy/)\n\n",
  "llmsTxtExisting": "# Atlan — The Context Layer for AI\n\n> Atlan is the Context Layer for AI: the governed infrastructure between your data stack and your AI systems. Enterprise AI doesn't fail because of the model — it fails because of missing context. Atlan builds the Enterprise Data Graph, a living metadata foundation that unifies 80+ sources with column-level lineage, and serves that context to every AI agent, workflow, and human that needs it. Trusted by Nasdaq, Autodesk, GitLab, and 500+ enterprise data teams globally.\n\nFor the complete index of every page, see https://atlan.com/sitemap.xml.\n\n## What is the Context Layer\n- [Context Layer for AI](https://atlan.com/context-layer/): Atlan's definitive explanation of the Context Layer — the governed tier between your data and your AI that makes enterprise models trustworthy, grounded, and auditable.\n- [Enterprise Context Layer](https://atlan.com/know/enterprise-context-layer/): Atlan's knowledge hub on the enterprise context layer — how it works, why it matters, and how modern data teams build one.\n- [Enterprise Data Graph](https://atlan.com/know/enterprise-data-graph/): How Atlan's Enterprise Data Graph works — unifying metadata from 80+ sources with column-level lineage into a governed, queryable context store AI agents can use at runtime.\n- [Atlan homepage](https://atlan.com/): Atlan's product overview — the Context Layer for AI, the problem of missing enterprise context, and the Enterprise Data Graph that solves it.\n\n## Product\n\nAtlan's Context Layer is built on the Enterprise Data Graph — a living\nfoundation unifying metadata from 80+ sources with column-level lineage\nacross the data estate. The products below turn that graph into context\nevery AI agent, application, and human can use.\n\n- [Context Agents](https://atlan.com/context-agents/): Atlan's AI teammates that write, maintain, and evolve tacit knowledge — ontology, descriptions, data quality metrics, glossary, and READMEs — making enterprise data AI-ready in 30 days.\n- [Context Engineering Studio](https://atlan.com/context-engineering-studio/): Atlan's studio where humans and AI build the shared world model enterprise AI requires — bootstrap, test, and ship context to Cortex, Genie, Claude, or Codex from one interface.\n- [Context Lakehouse](https://atlan.com/context-lakehouse/): Atlan's context store engineered natively for AI — Iceberg-native, open formats, graph-plus-file architecture, vector-native AI search.\n- [MCP Server](https://atlan.com/mcp-server/): Atlan's Model Context Protocol server — connects AI agents directly to the Enterprise Data Graph so LLMs can query lineage, glossary, and data quality at runtime.\n- [Data Marketplace](https://atlan.com/data-marketplace/): Atlan's self-serve discovery layer — business users find, understand, and request certified data assets with full context, inside Slack, Teams, or any AI assistant.\n- [Data Lineage](https://atlan.com/data-lineage/): Atlan's column-level, end-to-end lineage — the lineage backbone of the Enterprise Data Graph.\n- [Connectors & Apps](https://atlan.com/connectors/): Atlan's 80+ connectors that pull metadata from every business system into the Enterprise Data Graph.\n\n## Customer Stories\n\n- [CME Group](https://atlan.com/regovern-watch-center/cme-group-context-at-speed/): How CME Group built context at speed across its data estate using Atlan's Context Layer.\n- [DigiKey](https://atlan.com/regovern-watch-center/digikey-context-readiness/): How DigiKey achieved AI-readiness by building context with Atlan before deploying production AI systems.\n- [Virgin Media O2](https://atlan.com/regovern-watch-center/virgin-media-o2-context-for-all/): How Virgin Media O2 made governed context available to every team using Atlan's metadata infrastructure.\n- [Workday](https://atlan.com/regovern-watch-center/workday-context-as-culture/): How Workday embedded context into its data culture with Atlan's Context Layer platform.\n- [Mastercard](https://atlan.com/regovern-watch-center/mastercard-context-by-design/): How Mastercard built context by design as a foundation for enterprise AI, powered by Atlan.\n- [General Motors](https://atlan.com/regovern-watch-center/contracts-context-and-cars-general-motors/): How General Motors connected contracts, data governance, and operational context using Atlan.\n\n## Analyst Recognition\n\n- [Gartner Magic Quadrant 2026](https://atlan.com/gartner-magic-quadrant-data-governance-2026/): Atlan named a Leader in the 2026 Gartner® Magic Quadrant™ for Data & Analytics Governance Platforms.\n- [Forrester Wave Q3 2025](https://atlan.com/forrester-wave/): Atlan named a Leader and Customer Favorite in The Forrester Wave™ Data Governance Solutions, earning the highest score in 15 criteria.\n\n## Learn\n\n- [Product documentation](https://docs.atlan.com/): Official Atlan docs — connector setup, governance APIs, Context Agent configuration, and how-to guides for administrators and data engineers.\n- [Resources hub](https://atlan.com/resources/): Atlan's library of guides, ebooks, and frameworks on context engineering, AI governance, and data readiness.\n- [Context & Chaos](https://atlan.com/context-and-chaos/): Atlan's practitioner series — deep-dives on context layers, ontologies, and building AI-native data infrastructure in the enterprise.\n- [CIO's Guide to Context Graphs](https://atlan.com/resources/cio-guide-to-context-graphs/): Atlan's executive guide on why context graphs are becoming core AI infrastructure — for data leadership and CTOs.\n- [Context Layer Readiness Assessment](https://atlan.com/context-layer-readiness-assessment/): Atlan's self-assessment for measuring an organization's context-layer readiness before AI deployment.\n\n## Brand Guidelines\n\nThe living reference for Atlan's visual identity. Every guidelines page has a\nMarkdown twin — fetch the .md URLs below for clean, agent-readable content\nwithout site chrome. The complete set in one file: https://atlan.com/llms-full.txt\n\n- [Brand overview](https://atlan.com/brand.md): Index of Atlan's brand guidelines — logo, color, typography, iconography, and motion.\n- [Logo](https://atlan.com/brand/logo.md): Primary logotype, favicon, clearspace, QR codes, incorrect usage, and partnership lockups.\n- [Color](https://atlan.com/brand/color.md): Primary, secondary, tertiary, and neutral palettes, print values, pairings, and application examples.\n- [Typography](https://atlan.com/brand/typography.md): Funnel Display for headlines, Inter for everything else — the 2026 type scale.\n- [Iconography](https://atlan.com/brand/iconography.md): Rounded-stroke icon style, core color combinations, and specs.\n- [Motion](https://atlan.com/brand/motion.md): The choreography system — principles, easing curves, typographic animation, dynamic backgrounds, and logo animation.\n\n## Company\n\n- [About Atlan](https://atlan.com/about/): Atlan's company mission, origin story, and team — building governed AI infrastructure for modern data teams.\n- [Newsroom](https://atlan.com/newsroom/): Atlan's press releases, funding announcements, and product launch news.\n- [Partners](https://atlan.com/partners/): Atlan's technology and solution partner ecosystem.\n- [Careers](https://atlan.com/careers/): Open roles at Atlan.\n\n## Optional\n\n- [Talk to sales](https://atlan.com/forms/talk-to-sales-contact/): Contact Atlan's team to discuss enterprise deployment.\n- [Demo hub](https://atlan.com/demos/): Atlan's interactive and on-demand demos — context agent workflows, lineage exploration, and AI-ready data.\n- [Context Layer demo](https://atlan.com/context-layer-demo/): Focused demo of Atlan's Context Layer for AI.\n- [Context Agents demo](https://atlan.com/context-agents-demo/): Focused demo of Atlan's Context Agents.\n- [Context Engineering Studio demo](https://atlan.com/context-engineering-studio-demo/): Focused demo of Atlan's Context Engineering Studio.",
  "emergingProtocols": {
    "oauthProtectedResource": {
      "exists": false,
      "url": "https://atlan.com/.well-known/oauth-protected-resource"
    },
    "oauthDiscovery": {
      "exists": false,
      "url": "https://atlan.com/.well-known/oauth-authorization-server"
    },
    "mcpServerCard": {
      "exists": false,
      "url": "https://atlan.com/.well-known/mcp/server-card.json",
      "draft": true
    },
    "a2aAgentCard": {
      "exists": false,
      "url": "https://atlan.com/.well-known/agent-card.json"
    },
    "apiCatalog": {
      "exists": true,
      "url": "https://atlan.com/.well-known/api-catalog",
      "contentType": "binary/octet-stream",
      "validMediaType": false,
      "apis": 2
    },
    "agentSkills": {
      "exists": false,
      "url": "https://atlan.com/.well-known/agent-skills/index.json",
      "draft": true
    },
    "count": 1,
    "total": 6
  },
  "botAccess": {
    "probed": true,
    "bot": "OAI-SearchBot",
    "controlStatus": 200,
    "botStatus": 200,
    "discriminates": false,
    "refusedAsBot": false,
    "edge": "Cloudflare",
    "verifiable": false,
    "detail": "This origin answers OAI-SearchBot exactly as it answers any other client (200). No edge-level filtering of AI crawlers observed."
  },
  "snippets": [
    {
      "id": "add_markdown_negotiation",
      "title": "Support Markdown for Agents",
      "description": "Let AI agents request a clean Markdown version of any page via content negotiation, .md alternate URLs, link tags or Link headers.",
      "language": "html",
      "code": "<!-- Mechanism 3: link tag advertising the .md alternate -->\n<link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">",
      "filename": "<head>",
      "stacks": [
        {
          "id": "html",
          "label": "HTML <head>",
          "language": "html",
          "filename": "<head>",
          "code": "<!-- Mechanism 3: link tag advertising the .md alternate -->\n<link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">"
        },
        {
          "id": "express",
          "label": "Express",
          "language": "javascript",
          "filename": "server.js",
          "code": "// Mechanisms 1 + 4: content negotiation + Link header\napp.get('/page', (req, res) => {\n  res.setHeader('Vary', 'Accept');\n  res.setHeader('Link', '</page.md>; rel=\"alternate\"; type=\"text/markdown\"');\n  if ((req.headers.accept || '').includes('text/markdown')) {\n    res.type('text/markdown; charset=utf-8');\n    return res.send(renderMarkdown('page'));\n  }\n  res.render('page');\n});"
        },
        {
          "id": "fastify",
          "label": "Fastify",
          "language": "javascript",
          "filename": "server.js",
          "code": "// Mechanisms 1 + 4: content negotiation + Link header\nfastify.get('/page', async (req, reply) => {\n  reply.header('Vary', 'Accept');\n  reply.header('Link', '</page.md>; rel=\"alternate\"; type=\"text/markdown\"');\n  if ((req.headers.accept || '').includes('text/markdown')) {\n    return reply.type('text/markdown; charset=utf-8').send(renderMarkdown('page'));\n  }\n  return reply.view('/page.ejs');\n});"
        },
        {
          "id": "nextjs",
          "label": "Next.js",
          "language": "typescript",
          "filename": "app/page/route.ts",
          "code": "// Next.js App Router — Route Handler returning Markdown\nimport { NextRequest } from 'next/server';\nimport { renderMarkdown } from '@/lib/md';\nexport async function GET(req: NextRequest) {\n  const accept = req.headers.get('accept') || '';\n  if (accept.includes('text/markdown')) {\n    return new Response(await renderMarkdown('page'), {\n      headers: {\n        'Content-Type': 'text/markdown; charset=utf-8',\n        'Vary': 'Accept',\n      },\n    });\n  }\n  // Fall through to the page component\n  return new Response(null, { status: 404 });\n}"
        },
        {
          "id": "wordpress",
          "label": "WordPress",
          "language": "php",
          "filename": "functions.php",
          "code": "<?php\n// Mechanism 1: respond to Accept: text/markdown on the same URL\nadd_action('template_redirect', function () {\n    if (!is_singular()) return;\n    $accept = $_SERVER['HTTP_ACCEPT'] ?? '';\n    if (strpos($accept, 'text/markdown') === false) return;\n    header('Content-Type: text/markdown; charset=utf-8');\n    header('Vary: Accept');\n    $post = get_queried_object();\n    echo \"# \" . get_the_title($post) . \"\\n\\n\";\n    echo wp_strip_all_tags(apply_filters('the_content', $post->post_content));\n    exit;\n});"
        },
        {
          "id": "static",
          "label": "Hugo / Jekyll / Astro",
          "language": "txt",
          "filename": "static/page.md",
          "code": "# Mechanism 2: serve .md alongside .html\n# Hugo: place page.md in /static/ — built unchanged\n# Jekyll: drop page.md in /assets/ — copied as-is\n# Astro: src/pages/page.md.ts that exports a GET returning markdown\n\n# Then advertise with mechanism 3 in <head>:\n#   <link rel=\"alternate\" type=\"text/markdown\" href=\"/page.md\">"
        }
      ]
    }
  ]
}

Use our API to get this programmatically (coming soon)